Uncovering suppressed travel: A scoping review of surveys measuring unmet transportation need
Bibliographic record
Abstract
Unrealized travel and its associated activity participation is included in many overlapping concepts in the literature—unmet need, latent demand, suppressed travel, and forgone travel. In this scoping review, we focus on suppressed travel , which we define as travel, and associated activity participation, that is unrealized due to transportation-related social exclusion and associated mobility barriers. We review how researchers have measured suppressed travel using surveys, identifying which populations are studied (who), which destinations are considered (where), how questions are structured (how), which barriers are identified as causes of suppressed travel (why), and to what extent questions address suppressed desires and mobility horizons (what). We also assess study quality. We conducted a search using sets of keywords relating to equity, transportation, and surveys. We identified 3,522 unique abstracts from Web of Science and Scopus published since the year 2000. Two undergraduate reviewers independently screened the abstracts with author oversight. The authors conducted full-text reviews of 533 remaining studies. Of these, 158 survived to data extraction, and 19 of those could ultimately be included in this analysis. We find strong evidence of travel suppression among older adults and people with disabilities. Insufficient transit service and dependency on others for rides are identified as primary causes. Among the general population, unrealized travel is greater for leisure and eating out trips, while populations experiencing TRSE face travel suppression for work, education, and other essential trips. We identify gaps in populations and causes studied and conclude with recommendations on how to advance knowledge in this area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.274 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.040 | 0.042 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".